SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
Authors: BANG AN, Xun Zhou, YONGJIAN ZHONG, Tianbao Yang
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on three real-world datasets demonstrate that Spatial Rank can effectively identify the top riskiest locations of crimes and traffic accidents and outperform state-of-the-art methods in terms of NDCG by up to 12.7%. |
| Researcher Affiliation | Academia | Bang An Department of Business Analytics University of Iowa Iowa City, IA 52242 EMAIL Xun Zhou Department of Business Analytics University of Iowa Iowa City, IA 52242 EMAIL Yongjian Zhong Department of Computer Science University of Iowa Iowa City, IA 52242 EMAIL Tianbao Yang Department of Computer Science and Engineering Texas A&M University College Station, TX 77843 EMAIL |
| Pseudocode | Yes | Algorithm 1: Spatial Rank Training |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the described methodology or explicitly state that the code is publicly available. |
| Open Datasets | Yes | We perform comprehensive experiments on three real-world traffic accident and crime datasets from Chicago2 and the State of Iowa3. 2https://data.cityofchicago.org/Transportation/Traffic-Crashes-Crashes/85ca-t3if 3https://icat.iowadot.gov/ |
| Dataset Splits | Yes | In the Chicago dataset, we collect data from the year 2019 to the year 2021. The first 18 months of this period are used as the training set, and the last 6 months of 2020 are used as the validating set. The year 2021 is used as a testing set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |